Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells9976
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory152.0 B

Variable types

Numeric11
Categorical8

Warnings

Quantidade de Restritivos is highly correlated with Modelo Score 2High correlation
Modelo Score 1 is highly correlated with Modelo Score 2 and 2 other fieldsHigh correlation
Modelo Score 2 is highly correlated with Quantidade de Restritivos and 3 other fieldsHigh correlation
Modelo Score 3 is highly correlated with Modelo Score 1 and 2 other fieldsHigh correlation
Modelo Score 4 is highly correlated with Modelo Score 1 and 2 other fieldsHigh correlation
Quantidade de Restritivos is highly correlated with Valor dos Restritivos and 3 other fieldsHigh correlation
Valor dos Restritivos is highly correlated with Quantidade de Restritivos and 3 other fieldsHigh correlation
Quantidade de Protestos is highly correlated with Valor dos ProtestosHigh correlation
Valor dos Protestos is highly correlated with Quantidade de ProtestosHigh correlation
Modelo Score 1 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 2 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 3 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 4 is highly correlated with Modelo Score 1 and 2 other fieldsHigh correlation
Quantidade de Restritivos is highly correlated with Valor dos Restritivos and 3 other fieldsHigh correlation
Valor dos Restritivos is highly correlated with Quantidade de Restritivos and 3 other fieldsHigh correlation
Quantidade de Protestos is highly correlated with Valor dos ProtestosHigh correlation
Valor dos Protestos is highly correlated with Quantidade de ProtestosHigh correlation
Modelo Score 1 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 2 is highly correlated with Quantidade de Restritivos and 3 other fieldsHigh correlation
Modelo Score 3 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 4 is highly correlated with Modelo Score 1 and 1 other fieldsHigh correlation
Performance 60D9M EVER is highly correlated with Performance 90D9M EVER and 3 other fieldsHigh correlation
Quantidade de Restritivos is highly correlated with Endividamento and 3 other fieldsHigh correlation
Endividamento is highly correlated with Quantidade de Restritivos and 3 other fieldsHigh correlation
Performance 90D9M EVER is highly correlated with Performance 60D9M EVER and 3 other fieldsHigh correlation
Modelo Score 1 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 4 is highly correlated with Modelo Score 1 and 2 other fieldsHigh correlation
Performance 90D12M EVER is highly correlated with Performance 60D9M EVER and 3 other fieldsHigh correlation
Performance 60D6M EVER is highly correlated with Performance 60D9M EVER and 3 other fieldsHigh correlation
Performance 30D3M EVER is highly correlated with Performance 60D9M EVER and 3 other fieldsHigh correlation
Modelo Score 2 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Modelo Score 3 is highly correlated with Quantidade de Restritivos and 4 other fieldsHigh correlation
Performance 60D9M EVER is highly correlated with Performance 60D6M EVER and 3 other fieldsHigh correlation
Performance 60D6M EVER is highly correlated with Performance 60D9M EVER and 3 other fieldsHigh correlation
Performance 30D3M EVER is highly correlated with Performance 60D9M EVER and 1 other fieldsHigh correlation
Performance 90D9M EVER is highly correlated with Performance 60D9M EVER and 2 other fieldsHigh correlation
Performance 90D12M EVER is highly correlated with Performance 60D9M EVER and 2 other fieldsHigh correlation
Performance 60D9M EVER has 1665 (16.7%) missing values Missing
Performance 90D9M EVER has 1665 (16.7%) missing values Missing
Performance 90D12M EVER has 6646 (66.5%) missing values Missing
Valor dos Restritivos is highly skewed (γ1 = 21.08327087) Skewed
Valor dos Protestos is highly skewed (γ1 = 30.00032901) Skewed
Nº do cliente is uniformly distributed Uniform
Nº do cliente has unique values Unique
Quantidade de Cheques sem Fundo has 9687 (96.9%) zeros Zeros
Quantidade de Restritivos has 7149 (71.5%) zeros Zeros
Valor dos Restritivos has 7169 (71.7%) zeros Zeros
Quantidade de Protestos has 9446 (94.5%) zeros Zeros
Valor dos Protestos has 9446 (94.5%) zeros Zeros

Reproduction

Analysis started2021-09-26 08:35:24.045493
Analysis finished2021-09-26 08:36:00.830233
Duration36.78 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Nº do cliente
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610115364.5
Minimum1241258
Maximum1218989471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:01.041231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1241258
5-th percentile62128668.65
Q1305678311.2
median610115364.5
Q3914552417.8
95-th percentile1158102060
Maximum1218989471
Range1217748213
Interquartile range (IQR)608874106.5

Descriptive statistics

Standard deviation351586364.2
Coefficient of variation (CV)0.5762621049
Kurtosis-1.2
Mean610115364.5
Median Absolute Deviation (MAD)304467500
Skewness3.163326862 × 10-19
Sum6.101153645 × 1012
Variance1.236129715 × 1017
MonotonicityNot monotonic
2021-09-26T05:36:01.316252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6325850661
 
< 0.1%
198746691
 
< 0.1%
7177141791
 
< 0.1%
9696914821
 
< 0.1%
5760758981
 
< 0.1%
120803011
 
< 0.1%
9863763011
 
< 0.1%
4036255061
 
< 0.1%
6151695251
 
< 0.1%
3536928361
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
12412581
< 0.1%
13630451
< 0.1%
14848321
< 0.1%
16066191
< 0.1%
17284061
< 0.1%
18501931
< 0.1%
19719801
< 0.1%
20937671
< 0.1%
22155541
< 0.1%
23373411
< 0.1%
ValueCountFrequency (%)
12189894711
< 0.1%
12188676841
< 0.1%
12187458971
< 0.1%
12186241101
< 0.1%
12185023231
< 0.1%
12183805361
< 0.1%
12182587491
< 0.1%
12181369621
< 0.1%
12180151751
< 0.1%
12178933881
< 0.1%

Safra
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2020-05-01 00:00:00 UTC
1699 
2020-07-01 00:00:00 UTC
1668 
2020-10-01 00:00:00 UTC
1665 
2020-09-01 00:00:00 UTC
1659 
2020-06-01 00:00:00 UTC
1655 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters230000
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-05-01 00:00:00 UTC
2nd row2020-05-01 00:00:00 UTC
3rd row2020-06-01 00:00:00 UTC
4th row2020-05-01 00:00:00 UTC
5th row2020-10-01 00:00:00 UTC

Common Values

ValueCountFrequency (%)
2020-05-01 00:00:00 UTC1699
17.0%
2020-07-01 00:00:00 UTC1668
16.7%
2020-10-01 00:00:00 UTC1665
16.7%
2020-09-01 00:00:00 UTC1659
16.6%
2020-06-01 00:00:00 UTC1655
16.6%
2020-08-01 00:00:00 UTC1654
16.5%

Length

2021-09-26T05:36:02.097264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:02.224244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
00:00:0010000
33.3%
utc10000
33.3%
2020-05-011699
 
5.7%
2020-07-011668
 
5.6%
2020-10-011665
 
5.5%
2020-09-011659
 
5.5%
2020-06-011655
 
5.5%
2020-08-011654
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0100000
43.5%
220000
 
8.7%
-20000
 
8.7%
20000
 
8.7%
:20000
 
8.7%
111665
 
5.1%
U10000
 
4.3%
T10000
 
4.3%
C10000
 
4.3%
51699
 
0.7%
Other values (4)6636
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number140000
60.9%
Uppercase Letter30000
 
13.0%
Dash Punctuation20000
 
8.7%
Space Separator20000
 
8.7%
Other Punctuation20000
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100000
71.4%
220000
 
14.3%
111665
 
8.3%
51699
 
1.2%
71668
 
1.2%
91659
 
1.2%
61655
 
1.2%
81654
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
U10000
33.3%
T10000
33.3%
C10000
33.3%
Dash Punctuation
ValueCountFrequency (%)
-20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Other Punctuation
ValueCountFrequency (%)
:20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common200000
87.0%
Latin30000
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0100000
50.0%
220000
 
10.0%
-20000
 
10.0%
20000
 
10.0%
:20000
 
10.0%
111665
 
5.8%
51699
 
0.8%
71668
 
0.8%
91659
 
0.8%
61655
 
0.8%
Latin
ValueCountFrequency (%)
U10000
33.3%
T10000
33.3%
C10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII230000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0100000
43.5%
220000
 
8.7%
-20000
 
8.7%
20000
 
8.7%
:20000
 
8.7%
111665
 
5.1%
U10000
 
4.3%
T10000
 
4.3%
C10000
 
4.3%
51699
 
0.7%
Other values (4)6636
 
2.9%

Estado
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
RS
1377 
SP
1289 
MS
1209 
BA
1073 
MG
1009 
Other values (5)
4043 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMS
2nd rowMG
3rd rowMS
4th rowRS
5th rowRJ

Common Values

ValueCountFrequency (%)
RS1377
13.8%
SP1289
12.9%
MS1209
12.1%
BA1073
10.7%
MG1009
10.1%
PR921
9.2%
GO841
8.4%
SC807
8.1%
RJ753
7.5%
TO721
7.2%

Length

2021-09-26T05:36:02.631243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:02.781242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rs1377
13.8%
sp1289
12.9%
ms1209
12.1%
ba1073
10.7%
mg1009
10.1%
pr921
9.2%
go841
8.4%
sc807
8.1%
rj753
7.5%
to721
7.2%

Most occurring characters

ValueCountFrequency (%)
S4682
23.4%
R3051
15.3%
M2218
11.1%
P2210
11.1%
G1850
 
9.2%
O1562
 
7.8%
B1073
 
5.4%
A1073
 
5.4%
C807
 
4.0%
J753
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter20000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S4682
23.4%
R3051
15.3%
M2218
11.1%
P2210
11.1%
G1850
 
9.2%
O1562
 
7.8%
B1073
 
5.4%
A1073
 
5.4%
C807
 
4.0%
J753
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin20000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S4682
23.4%
R3051
15.3%
M2218
11.1%
P2210
11.1%
G1850
 
9.2%
O1562
 
7.8%
B1073
 
5.4%
A1073
 
5.4%
C807
 
4.0%
J753
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S4682
23.4%
R3051
15.3%
M2218
11.1%
P2210
11.1%
G1850
 
9.2%
O1562
 
7.8%
B1073
 
5.4%
A1073
 
5.4%
C807
 
4.0%
J753
 
3.8%

Renda Mensal
Real number (ℝ≥0)

Distinct1405
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2780.1557
Minimum1041
Maximum33564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:03.007250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1041
5-th percentile1105.95
Q11404
median3365
Q33492
95-th percentile4407
Maximum33564
Range32523
Interquartile range (IQR)2088

Descriptive statistics

Standard deviation1464.592111
Coefficient of variation (CV)0.5268021898
Kurtosis58.40624455
Mean2780.1557
Median Absolute Deviation (MAD)918
Skewness3.951733002
Sum27801557
Variance2145030.05
MonotonicityNot monotonic
2021-09-26T05:36:03.279243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34921232
 
12.3%
3366437
 
4.4%
2518315
 
3.1%
3365306
 
3.1%
3367303
 
3.0%
4404247
 
2.5%
3368193
 
1.9%
2391145
 
1.5%
4143132
 
1.3%
4277116
 
1.2%
Other values (1395)6574
65.7%
ValueCountFrequency (%)
10415
 
0.1%
10427
 
0.1%
104316
 
0.2%
10448
 
0.1%
104562
0.6%
10467
 
0.1%
10475
 
0.1%
10487
 
0.1%
10496
 
0.1%
10507
 
0.1%
ValueCountFrequency (%)
335641
< 0.1%
309321
< 0.1%
287691
< 0.1%
239071
< 0.1%
235831
< 0.1%
192371
< 0.1%
168311
< 0.1%
160091
< 0.1%
148581
< 0.1%
144991
< 0.1%

Endividamento
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Até 25%
7850 
Acima de 100%
1257 
26 a 50%
 
422
51 a 75%
 
287
76 a 100%
 
184

Length

Max length13
Median length7
Mean length7.8619
Min length7

Characters and Unicode

Total characters78619
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26 a 50%
2nd rowAté 25%
3rd rowAcima de 100%
4th rowAté 25%
5th rowAté 25%

Common Values

ValueCountFrequency (%)
Até 25%7850
78.5%
Acima de 100%1257
 
12.6%
26 a 50%422
 
4.2%
51 a 75%287
 
2.9%
76 a 100%184
 
1.8%

Length

2021-09-26T05:36:03.740266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:03.890264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
até7850
35.4%
257850
35.4%
1001441
 
6.5%
acima1257
 
5.7%
de1257
 
5.7%
a893
 
4.0%
26422
 
1.9%
50422
 
1.9%
51287
 
1.3%
75287
 
1.3%

Most occurring characters

ValueCountFrequency (%)
12150
15.5%
%10000
12.7%
A9107
11.6%
58846
11.3%
28272
10.5%
t7850
10.0%
é7850
10.0%
03304
 
4.2%
a2150
 
2.7%
11728
 
2.2%
Other values (7)7362
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24135
30.7%
Decimal Number23227
29.5%
Space Separator12150
15.5%
Other Punctuation10000
12.7%
Uppercase Letter9107
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t7850
32.5%
é7850
32.5%
a2150
 
8.9%
c1257
 
5.2%
i1257
 
5.2%
m1257
 
5.2%
d1257
 
5.2%
e1257
 
5.2%
Decimal Number
ValueCountFrequency (%)
58846
38.1%
28272
35.6%
03304
 
14.2%
11728
 
7.4%
6606
 
2.6%
7471
 
2.0%
Space Separator
ValueCountFrequency (%)
12150
100.0%
Other Punctuation
ValueCountFrequency (%)
%10000
100.0%
Uppercase Letter
ValueCountFrequency (%)
A9107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common45377
57.7%
Latin33242
42.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A9107
27.4%
t7850
23.6%
é7850
23.6%
a2150
 
6.5%
c1257
 
3.8%
i1257
 
3.8%
m1257
 
3.8%
d1257
 
3.8%
e1257
 
3.8%
Common
ValueCountFrequency (%)
12150
26.8%
%10000
22.0%
58846
19.5%
28272
18.2%
03304
 
7.3%
11728
 
3.8%
6606
 
1.3%
7471
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70769
90.0%
Latin 1 Sup7850
 
10.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12150
17.2%
%10000
14.1%
A9107
12.9%
58846
12.5%
28272
11.7%
t7850
11.1%
03304
 
4.7%
a2150
 
3.0%
11728
 
2.4%
c1257
 
1.8%
Other values (6)6105
8.6%
Latin 1 Sup
ValueCountFrequency (%)
é7850
100.0%

Quantidade de Cheques sem Fundo
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1649
Minimum0
Maximum64
Zeros9687
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:04.052264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.56414598
Coefficient of variation (CV)9.485421343
Kurtosis458.3460055
Mean0.1649
Median Absolute Deviation (MAD)0
Skewness17.79922507
Sum1649
Variance2.446552645
MonotonicityNot monotonic
2021-09-26T05:36:04.248244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
09687
96.9%
1112
 
1.1%
246
 
0.5%
334
 
0.3%
516
 
0.2%
615
 
0.1%
414
 
0.1%
712
 
0.1%
109
 
0.1%
117
 
0.1%
Other values (20)48
 
0.5%
ValueCountFrequency (%)
09687
96.9%
1112
 
1.1%
246
 
0.5%
334
 
0.3%
414
 
0.1%
516
 
0.2%
615
 
0.1%
712
 
0.1%
85
 
0.1%
96
 
0.1%
ValueCountFrequency (%)
641
 
< 0.1%
401
 
< 0.1%
341
 
< 0.1%
331
 
< 0.1%
312
< 0.1%
261
 
< 0.1%
253
< 0.1%
231
 
< 0.1%
222
< 0.1%
212
< 0.1%

Quantidade de Restritivos
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7111
Minimum-7
Maximum35
Zeros7149
Zeros (%)71.5%
Negative27
Negative (%)0.3%
Memory size78.2 KiB
2021-09-26T05:36:04.456265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum35
Range42
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.823285284
Coefficient of variation (CV)2.564034994
Kurtosis50.50499823
Mean0.7111
Median Absolute Deviation (MAD)0
Skewness5.493588771
Sum7111
Variance3.324369227
MonotonicityNot monotonic
2021-09-26T05:36:04.643250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
07149
71.5%
11350
 
13.5%
2590
 
5.9%
3317
 
3.2%
4188
 
1.9%
5121
 
1.2%
678
 
0.8%
756
 
0.6%
833
 
0.3%
925
 
0.2%
Other values (19)93
 
0.9%
ValueCountFrequency (%)
-71
 
< 0.1%
-61
 
< 0.1%
-41
 
< 0.1%
-32
 
< 0.1%
-122
 
0.2%
07149
71.5%
11350
 
13.5%
2590
 
5.9%
3317
 
3.2%
4188
 
1.9%
ValueCountFrequency (%)
351
 
< 0.1%
271
 
< 0.1%
263
< 0.1%
203
< 0.1%
196
0.1%
182
 
< 0.1%
171
 
< 0.1%
166
0.1%
152
 
< 0.1%
146
0.1%

Valor dos Restritivos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1997
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1635.6634
Minimum0
Maximum462266
Zeros7169
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:04.876264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3145
95-th percentile5888.85
Maximum462266
Range462266
Interquartile range (IQR)145

Descriptive statistics

Standard deviation10741.23154
Coefficient of variation (CV)6.566896062
Kurtosis691.8389731
Mean1635.6634
Median Absolute Deviation (MAD)0
Skewness21.08327087
Sum16356634
Variance115374055
MonotonicityNot monotonic
2021-09-26T05:36:05.111264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07169
71.7%
939
 
0.1%
2648
 
0.1%
1498
 
0.1%
697
 
0.1%
1507
 
0.1%
1597
 
0.1%
876
 
0.1%
1466
 
0.1%
1736
 
0.1%
Other values (1987)2767
 
27.7%
ValueCountFrequency (%)
07169
71.7%
21
 
< 0.1%
31
 
< 0.1%
53
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
151
 
< 0.1%
173
 
< 0.1%
181
 
< 0.1%
202
 
< 0.1%
ValueCountFrequency (%)
4622661
< 0.1%
4348371
< 0.1%
2197411
< 0.1%
2139851
< 0.1%
2058991
< 0.1%
1791471
< 0.1%
1505661
< 0.1%
1470351
< 0.1%
1459631
< 0.1%
1415671
< 0.1%

Quantidade de Protestos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1529
Minimum0
Maximum37
Zeros9446
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:05.329246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.173907746
Coefficient of variation (CV)7.677617697
Kurtosis362.0661673
Mean0.1529
Median Absolute Deviation (MAD)0
Skewness16.57073151
Sum1529
Variance1.378059396
MonotonicityNot monotonic
2021-09-26T05:36:05.519244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
09446
94.5%
1311
 
3.1%
2113
 
1.1%
332
 
0.3%
427
 
0.3%
614
 
0.1%
59
 
0.1%
79
 
0.1%
96
 
0.1%
85
 
0.1%
Other values (14)28
 
0.3%
ValueCountFrequency (%)
09446
94.5%
1311
 
3.1%
2113
 
1.1%
332
 
0.3%
427
 
0.3%
59
 
0.1%
614
 
0.1%
79
 
0.1%
85
 
0.1%
96
 
0.1%
ValueCountFrequency (%)
371
 
< 0.1%
332
< 0.1%
251
 
< 0.1%
243
< 0.1%
222
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
163
< 0.1%
142
< 0.1%

Valor dos Protestos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct492
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.7243
Minimum0
Maximum133742
Zeros9446
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:05.749255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile150.15
Maximum133742
Range133742
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2700.805532
Coefficient of variation (CV)12.87788555
Kurtosis1169.532221
Mean209.7243
Median Absolute Deviation (MAD)0
Skewness30.00032901
Sum2097243
Variance7294350.522
MonotonicityNot monotonic
2021-09-26T05:36:06.008243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09446
94.5%
2007
 
0.1%
1304
 
< 0.1%
1204
 
< 0.1%
2963
 
< 0.1%
953
 
< 0.1%
6633
 
< 0.1%
2353
 
< 0.1%
4643
 
< 0.1%
3233
 
< 0.1%
Other values (482)521
 
5.2%
ValueCountFrequency (%)
09446
94.5%
251
 
< 0.1%
311
 
< 0.1%
341
 
< 0.1%
681
 
< 0.1%
701
 
< 0.1%
811
 
< 0.1%
852
 
< 0.1%
861
 
< 0.1%
891
 
< 0.1%
ValueCountFrequency (%)
1337421
< 0.1%
1170491
< 0.1%
888931
< 0.1%
685961
< 0.1%
513301
< 0.1%
490241
< 0.1%
486581
< 0.1%
434541
< 0.1%
385851
< 0.1%
377881
< 0.1%

Modelo Score 1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct969
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.1828
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:06.267244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1216
median481
Q3672.25
95-th percentile876
Maximum999
Range998
Interquartile range (IQR)456.25

Descriptive statistics

Standard deviation264.0135074
Coefficient of variation (CV)0.5749638432
Kurtosis-1.13309099
Mean459.1828
Median Absolute Deviation (MAD)231
Skewness-0.003480547069
Sum4591828
Variance69703.1321
MonotonicityNot monotonic
2021-09-26T05:36:06.523243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2215
 
2.1%
20187
 
0.9%
47371
 
0.7%
8761
 
0.6%
25059
 
0.6%
22057
 
0.6%
36650
 
0.5%
15850
 
0.5%
61243
 
0.4%
11442
 
0.4%
Other values (959)9265
92.7%
ValueCountFrequency (%)
117
 
0.2%
2215
2.1%
322
 
0.2%
45
 
0.1%
74
 
< 0.1%
83
 
< 0.1%
121
 
< 0.1%
1315
 
0.1%
168
 
0.1%
171
 
< 0.1%
ValueCountFrequency (%)
99935
0.4%
9981
 
< 0.1%
9971
 
< 0.1%
9963
 
< 0.1%
9951
 
< 0.1%
9941
 
< 0.1%
9916
 
0.1%
9871
 
< 0.1%
9861
 
< 0.1%
9851
 
< 0.1%

Modelo Score 2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct546
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.1838
Minimum1
Maximum811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:06.780245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile208
Q1253
median627
Q3669
95-th percentile715
Maximum811
Range810
Interquartile range (IQR)416

Descriptive statistics

Standard deviation200.4944334
Coefficient of variation (CV)0.3824888014
Kurtosis-0.9990197611
Mean524.1838
Median Absolute Deviation (MAD)55
Skewness-0.8393160333
Sum5241838
Variance40198.01782
MonotonicityNot monotonic
2021-09-26T05:36:07.137244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680115
 
1.1%
66792
 
0.9%
66391
 
0.9%
66985
 
0.9%
65880
 
0.8%
67278
 
0.8%
65377
 
0.8%
65074
 
0.7%
22373
 
0.7%
67670
 
0.7%
Other values (536)9165
91.6%
ValueCountFrequency (%)
168
0.7%
791
 
< 0.1%
811
 
< 0.1%
821
 
< 0.1%
891
 
< 0.1%
964
 
< 0.1%
971
 
< 0.1%
986
 
0.1%
991
 
< 0.1%
1012
 
< 0.1%
ValueCountFrequency (%)
8111
 
< 0.1%
7971
 
< 0.1%
7821
 
< 0.1%
7792
 
< 0.1%
7783
< 0.1%
7771
 
< 0.1%
7752
 
< 0.1%
7731
 
< 0.1%
7721
 
< 0.1%
7716
0.1%

Modelo Score 3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct756
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.2801
Minimum1
Maximum922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:07.461246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile188
Q1277
median482
Q3669
95-th percentile829
Maximum922
Range921
Interquartile range (IQR)392

Descriptive statistics

Standard deviation215.5618968
Coefficient of variation (CV)0.4460392571
Kurtosis-1.230702593
Mean483.2801
Median Absolute Deviation (MAD)199
Skewness0.1326896553
Sum4832801
Variance46466.93134
MonotonicityNot monotonic
2021-09-26T05:36:07.723244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266225
 
2.2%
250187
 
1.9%
258184
 
1.8%
235182
 
1.8%
243177
 
1.8%
282174
 
1.7%
274171
 
1.7%
290147
 
1.5%
227142
 
1.4%
297141
 
1.4%
Other values (746)8270
82.7%
ValueCountFrequency (%)
139
0.4%
701
 
< 0.1%
781
 
< 0.1%
821
 
< 0.1%
831
 
< 0.1%
862
 
< 0.1%
912
 
< 0.1%
921
 
< 0.1%
942
 
< 0.1%
1002
 
< 0.1%
ValueCountFrequency (%)
9222
 
< 0.1%
9211
 
< 0.1%
9202
 
< 0.1%
9191
 
< 0.1%
9145
0.1%
9131
 
< 0.1%
9112
 
< 0.1%
9102
 
< 0.1%
9081
 
< 0.1%
9061
 
< 0.1%

Modelo Score 4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct551
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean695.8885
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-09-26T05:36:08.017243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile527
Q1630
median700
Q3772
95-th percentile863
Maximum999
Range998
Interquartile range (IQR)142

Descriptive statistics

Standard deviation115.2371712
Coefficient of variation (CV)0.1655971771
Kurtosis7.53064024
Mean695.8885
Median Absolute Deviation (MAD)71
Skewness-1.428386636
Sum6958885
Variance13279.60563
MonotonicityNot monotonic
2021-09-26T05:36:08.303245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67375
 
0.8%
168
 
0.7%
71161
 
0.6%
67253
 
0.5%
64451
 
0.5%
69850
 
0.5%
73450
 
0.5%
71249
 
0.5%
74049
 
0.5%
76548
 
0.5%
Other values (541)9446
94.5%
ValueCountFrequency (%)
168
0.7%
3751
 
< 0.1%
3812
 
< 0.1%
3831
 
< 0.1%
4011
 
< 0.1%
4051
 
< 0.1%
4091
 
< 0.1%
4111
 
< 0.1%
4151
 
< 0.1%
4161
 
< 0.1%
ValueCountFrequency (%)
9995
0.1%
9921
 
< 0.1%
9891
 
< 0.1%
9851
 
< 0.1%
9811
 
< 0.1%
9741
 
< 0.1%
9721
 
< 0.1%
9713
< 0.1%
9701
 
< 0.1%
9691
 
< 0.1%

Performance 30D3M EVER
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
BOM
8756 
MAU
1244 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOM
2nd rowBOM
3rd rowBOM
4th rowMAU
5th rowBOM

Common Values

ValueCountFrequency (%)
BOM8756
87.6%
MAU1244
 
12.4%

Length

2021-09-26T05:36:08.772247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:08.912250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bom8756
87.6%
mau1244
 
12.4%

Most occurring characters

ValueCountFrequency (%)
M10000
33.3%
B8756
29.2%
O8756
29.2%
A1244
 
4.1%
U1244
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter30000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M10000
33.3%
B8756
29.2%
O8756
29.2%
A1244
 
4.1%
U1244
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin30000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M10000
33.3%
B8756
29.2%
O8756
29.2%
A1244
 
4.1%
U1244
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M10000
33.3%
B8756
29.2%
O8756
29.2%
A1244
 
4.1%
U1244
 
4.1%

Performance 60D6M EVER
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
BOM
8290 
MAU
1710 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAU
2nd rowBOM
3rd rowBOM
4th rowBOM
5th rowBOM

Common Values

ValueCountFrequency (%)
BOM8290
82.9%
MAU1710
 
17.1%

Length

2021-09-26T05:36:09.247244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:09.366244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bom8290
82.9%
mau1710
 
17.1%

Most occurring characters

ValueCountFrequency (%)
M10000
33.3%
B8290
27.6%
O8290
27.6%
A1710
 
5.7%
U1710
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter30000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M10000
33.3%
B8290
27.6%
O8290
27.6%
A1710
 
5.7%
U1710
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin30000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M10000
33.3%
B8290
27.6%
O8290
27.6%
A1710
 
5.7%
U1710
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M10000
33.3%
B8290
27.6%
O8290
27.6%
A1710
 
5.7%
U1710
 
5.7%

Performance 60D9M EVER
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1665
Missing (%)16.7%
Memory size78.2 KiB
BOM
6390 
MAU
1945 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25005
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAU
2nd rowBOM
3rd rowBOM
4th rowMAU
5th rowBOM

Common Values

ValueCountFrequency (%)
BOM6390
63.9%
MAU1945
 
19.4%
(Missing)1665
 
16.7%

Length

2021-09-26T05:36:09.682595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:09.832594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bom6390
76.7%
mau1945
 
23.3%

Most occurring characters

ValueCountFrequency (%)
M8335
33.3%
B6390
25.6%
O6390
25.6%
A1945
 
7.8%
U1945
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter25005
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M8335
33.3%
B6390
25.6%
O6390
25.6%
A1945
 
7.8%
U1945
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin25005
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M8335
33.3%
B6390
25.6%
O6390
25.6%
A1945
 
7.8%
U1945
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII25005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M8335
33.3%
B6390
25.6%
O6390
25.6%
A1945
 
7.8%
U1945
 
7.8%

Performance 90D9M EVER
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1665
Missing (%)16.7%
Memory size78.2 KiB
BOM
6770 
MAU
1565 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25005
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAU
2nd rowBOM
3rd rowBOM
4th rowBOM
5th rowBOM

Common Values

ValueCountFrequency (%)
BOM6770
67.7%
MAU1565
 
15.7%
(Missing)1665
 
16.7%

Length

2021-09-26T05:36:10.185617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:10.324594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bom6770
81.2%
mau1565
 
18.8%

Most occurring characters

ValueCountFrequency (%)
M8335
33.3%
B6770
27.1%
O6770
27.1%
A1565
 
6.3%
U1565
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter25005
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M8335
33.3%
B6770
27.1%
O6770
27.1%
A1565
 
6.3%
U1565
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Latin25005
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M8335
33.3%
B6770
27.1%
O6770
27.1%
A1565
 
6.3%
U1565
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII25005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M8335
33.3%
B6770
27.1%
O6770
27.1%
A1565
 
6.3%
U1565
 
6.3%

Performance 90D12M EVER
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing6646
Missing (%)66.5%
Memory size78.2 KiB
BOM
2558 
MAU
796 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10062
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAU
2nd rowBOM
3rd rowBOM
4th rowMAU
5th rowBOM

Common Values

ValueCountFrequency (%)
BOM2558
 
25.6%
MAU796
 
8.0%
(Missing)6646
66.5%

Length

2021-09-26T05:36:10.699601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-26T05:36:10.851601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
bom2558
76.3%
mau796
 
23.7%

Most occurring characters

ValueCountFrequency (%)
M3354
33.3%
B2558
25.4%
O2558
25.4%
A796
 
7.9%
U796
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10062
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M3354
33.3%
B2558
25.4%
O2558
25.4%
A796
 
7.9%
U796
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin10062
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M3354
33.3%
B2558
25.4%
O2558
25.4%
A796
 
7.9%
U796
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M3354
33.3%
B2558
25.4%
O2558
25.4%
A796
 
7.9%
U796
 
7.9%

Interactions

2021-09-26T05:35:29.269475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:29.481494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:29.695494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:29.914475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:30.112473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:30.312495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:30.713547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:30.941568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:31.146569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:31.750621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:32.038617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:32.287617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:32.585637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:32.835617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:33.138621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:33.380617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:33.653618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:33.893617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:34.111636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:34.316616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:34.665618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:35.020144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:35.229162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:35.447144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:35.661147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:35.878142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:36.066144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:36.264140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:36.473161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:36.687141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:36.893499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:37.104501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:37.307500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:37.534500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:37.734505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:37.924518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:38.113499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:38.292518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:38.474506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:38.665501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:38.867504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.050519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.243519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.419498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.598504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.800519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:39.999489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:40.192488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:40.370490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:40.563487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:40.765488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:41.273490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:41.471490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:41.673491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:41.872508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:42.058488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:42.269129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:42.482110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:42.711110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:42.934111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:43.147108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:43.372109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:43.588125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:43.804109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:44.056115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:44.268119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:44.507110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:44.747116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:44.987125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:45.199129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:45.410116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:45.643129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:45.870115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:46.089128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:46.312129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:46.560128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:46.769129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:46.974108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:47.186110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:47.406109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:47.621114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:47.815108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:48.009128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:48.217108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:48.425114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:48.609109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:48.836109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:49.029109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:49.229113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:49.469128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:50.091250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:50.322249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:50.524250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:50.811232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:51.265230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:51.518237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:51.755235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:51.981250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:52.217232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:52.507231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:52.801230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:53.114234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:53.454232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:53.757231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:53.996232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:54.266231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:54.483229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:54.701236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:54.927251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:55.131228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:55.391231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:55.694229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:56.020231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:56.256252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:56.458232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:56.650250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:56.878232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:57.242234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:57.517239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:57.865237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-26T05:35:58.325231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-09-26T05:36:10.982594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-26T05:36:11.363723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-26T05:36:11.713730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-26T05:36:12.132724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-26T05:36:12.652732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-26T05:35:59.195230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-26T05:35:59.881230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-26T05:36:00.344236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-26T05:36:00.569250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Nº do clienteSafraEstadoRenda MensalEndividamentoQuantidade de Cheques sem FundoQuantidade de RestritivosValor dos RestritivosQuantidade de ProtestosValor dos ProtestosModelo Score 1Modelo Score 2Modelo Score 3Modelo Score 4Performance 30D3M EVERPerformance 60D6M EVERPerformance 60D9M EVERPerformance 90D9M EVERPerformance 90D12M EVER
06.325851e+082020-05-01 00:00:00 UTCMS1076.026 a 50%0.02.0418.00.00.0141.0236.0196.0405.0BOMMAUMAUMAUMAU
11.190978e+092020-05-01 00:00:00 UTCMG12709.0Até 25%0.00.00.00.00.0767.0646.0805.0946.0BOMBOMBOMBOMBOM
21.089165e+092020-06-01 00:00:00 UTCMS2518.0Acima de 100%0.04.032306.00.00.067.0238.0196.0563.0BOMBOMBOMBOMBOM
31.342327e+082020-05-01 00:00:00 UTCRS1090.0Até 25%0.00.00.00.00.0245.0523.0290.0698.0MAUBOMMAUBOMMAU
41.055186e+092020-10-01 00:00:00 UTCRJ1155.0Até 25%0.00.00.00.00.0697.0718.0645.0816.0BOMBOMNaNNaNNaN
59.746847e+082020-06-01 00:00:00 UTCMG2518.0Até 25%0.00.00.00.00.0901.0681.0827.0884.0BOMBOMBOMBOMBOM
65.720569e+082020-09-01 00:00:00 UTCBA4278.0Até 25%0.00.00.00.00.0683.0679.0680.0772.0BOMBOMBOMBOMNaN
74.931390e+082020-10-01 00:00:00 UTCTO4548.0Até 25%0.00.00.00.00.0837.0659.0676.0782.0BOMBOMNaNNaNNaN
81.529879e+082020-06-01 00:00:00 UTCMG1723.0Até 25%0.00.00.00.00.0433.0614.0521.0698.0BOMBOMBOMBOMBOM
96.879982e+082020-06-01 00:00:00 UTCMG1958.0Acima de 100%0.09.06856.01.0106.0141.0212.0266.0645.0BOMBOMBOMBOMBOM

Last rows

Nº do clienteSafraEstadoRenda MensalEndividamentoQuantidade de Cheques sem FundoQuantidade de RestritivosValor dos RestritivosQuantidade de ProtestosValor dos ProtestosModelo Score 1Modelo Score 2Modelo Score 3Modelo Score 4Performance 30D3M EVERPerformance 60D6M EVERPerformance 60D9M EVERPerformance 90D9M EVERPerformance 90D12M EVER
99907.252650e+082020-05-01 00:00:00 UTCTO2391.0Acima de 100%0.03.04686.00.00.088.0230.0243.0617.0BOMBOMBOMBOMBOM
99915.464817e+082020-08-01 00:00:00 UTCTO1128.026 a 50%0.01.0441.00.00.025.0218.0188.0631.0BOMBOMBOMBOMNaN
99921.062859e+092020-06-01 00:00:00 UTCRS3627.0Até 25%0.00.00.00.00.0799.0714.0748.0808.0BOMBOMBOMBOMBOM
99931.939083e+082020-08-01 00:00:00 UTCPR4278.0Até 25%0.00.00.01.0199.0296.0696.0765.0687.0BOMBOMBOMBOMNaN
99941.034848e+092020-06-01 00:00:00 UTCPR4404.051 a 75%0.014.01165.00.00.0133.0229.0219.0579.0BOMBOMMAUMAUMAU
99956.965247e+062020-07-01 00:00:00 UTCMS4281.0Até 25%0.00.00.00.00.0574.0614.0418.0761.0MAUMAUMAUMAUNaN
99963.511353e+082020-07-01 00:00:00 UTCMG4274.0Até 25%0.02.0352.00.00.0126.0213.0243.0531.0BOMBOMBOMBOMNaN
99977.839663e+082020-05-01 00:00:00 UTCRS3365.0Até 25%0.00.00.00.00.0627.0649.0491.0655.0BOMBOMBOMBOMBOM
99984.603782e+082020-06-01 00:00:00 UTCSC2393.0Até 25%0.00.00.00.00.0605.0658.0740.0892.0BOMBOMBOMBOMBOM
99993.456549e+082020-10-01 00:00:00 UTCTO1292.0Até 25%0.00.00.00.00.0572.0665.0548.0711.0BOMBOMNaNNaNNaN